Dynamic Motion Research Articles

From Collective Mind to Communication

"Collective mind" is introduced as a set of simple intelligent units (say, neurons, or interacting agents) that can communicate by exchanging information without explicit global control. Incomplete information is compensated for by a sequence of random guesses symmetrically distributed around expectations with prescribed variances. Both the expectations and variances are the invariants characterizing the whole class of agents. These invariants are stored as parameters of the collective mind, while they contribute to dynamical formalism of the agents' evolution, and in particular, to the reflective chains of their nested abstract images of the selves and nonselves. The proposed model consists of the system of stochastic differential equations in the Langevin form to represent motor dynamics, and the corresponding Fokker-Planck equation to represent mental dynamics. The main departure of this model from newtonian and statistical physics is due to feedback from the mental to the motor dynamics, which makes the Fokker-Planck equation nonlinear. Interpretations of this model from mathematical, physical, biological, and psychological viewpoints are discussed. The model is illustrated by the dynamics of a dialog.

Unlocking Cage‐Confined Cations Molecular Dynamics toward High‐Tc Perovskite Ferroelectrics

Cage‐confinement effect that imposes great constriction on the dynamic behaviors of guest molecules is an established platform for tailoring physical properties. Herein, the strategy of enhancing cage‐confinement effect to control molecular motion has been probed for the first time to exploit new high‐Tc ferroelectrics of 2D hybrid perovskites. By fine‐tailoring of the confined cations inside the perovskite cavities, we have successfully obtained new homologous ferroelectrics of (BA)2(MA)2Pb3Cl10 (1; BA =n‐butylamine, MA = methylamine) and (BA)2(EA)2Pb3Cl10 (2; EA = ethylamine). Intriguingly, this dynamics modulation of cage‐confined cations leads to a remarkable promotion of Curie temperature (Tc), boosting from 340 K (for 1) to 402 K (for 2). In situ solid‐state NMR spectroscopy and theoretical simulations on energy barrier confirm that the larger EA cation in 2 is subject to stronger confinement effect, of which potential energy barrier of molecular motion is ~3.5 times that of MA cation. This dynamic behavior greatly suppresses the dynamic motions of EA cation, while MA cation can easily accelerate motion and reach a fast motion limit, thus accounting for an enhancement of Tc (ΔT~62 K) in 2. The finding sheds light on the understanding of cage‐confined electric orders and the precise design of high‐performance ferroelectrics.

Unlocking Cage-Confined Cations Molecular Dynamics toward High-Tc Perovskite Ferroelectrics.

Cage-confinement effect that imposes great constriction on the dynamic behaviors of guest molecules is an established platform for tailoring physical properties. Herein, the strategy of enhancing cage-confinement effect to control molecular motion has been probed for the first time to exploit new high-Tc ferroelectrics of 2D hybrid perovskites. By fine-tailoring of the confined cations inside the perovskite cavities, we have successfully obtained new homologous ferroelectrics of (BA)2(MA)2Pb3Cl10 (1; BA =n-butylamine, MA = methylamine) and (BA)2(EA)2Pb3Cl10 (2; EA = ethylamine). Intriguingly, this dynamics modulation of cage-confined cations leads to a remarkable promotion of Curie temperature (Tc), boosting from 340 K (for 1) to 402 K (for 2). In situ solid-state NMR spectroscopy and theoretical simulations on energy barrier confirm that the larger EA cation in 2 is subject to stronger confinement effect, of which potential energy barrier of molecular motion is ~3.5 times that of MA cation. This dynamic behavior greatly suppresses the dynamic motions of EA cation, while MA cation can easily accelerate motion and reach a fast motion limit, thus accounting for an enhancement of Tc (ΔT~62 K) in 2. The finding sheds light on the understanding of cage-confined electric orders and the precise design of high-performance ferroelectrics.

Two-dimensionally stable self-organisation arises in simple schooling swimmers through hydrodynamic interactions

We present new constrained and free-swimming experiments and simulations in the inertial regime, with Reynolds number $\mbox{Re} = O(10^4)$ , of a pair of two-dimensional and three-dimensional pitching hydrofoils interacting in a minimal school. The hydrofoils have an out-of-phase synchronisation, and they are varied through in-line, staggered and side-by-side formations within the two-dimensional interaction plane. It is discovered that there is a two-dimensionally stable equilibrium point for a side-by-side formation. This formation is super-stable, meaning that hydrodynamic forces will passively maintain this formation even under external perturbations, and the school as a whole has no net forces acting on it that cause it to drift to one side or the other. Previously discovered one-dimensionally stable equilibria driven by wake vortex interactions are shown to be, in fact, two-dimensionally unstable, at least for an out-of-phase synchronisation. Additionally, it is discovered that a trailing-edge vortex mechanism provides the restorative force to stabilise a side-by-side formation. The stable equilibrium is further verified by experiments and simulations for freely swimming foils where dynamic recoil motions are present. When constrained, swimmers in compact side-by-side formations experience collective efficiency and thrust increases up to 40 % and 100 %, respectively, whereas slightly staggered formations output an even higher efficiency improvement of 84 %, with an 87 % increase in thrust. Freely swimming foils in a stable side-by-side formation show efficiency and speed enhancements of up to 9 % and 15 %, respectively. These newfound schooling performance and stability characteristics suggest that fluid-mediated equilibria may play a role in the control strategies of schooling fish and fish-inspired robots.

A Personal Journey in Nanoscience via Developing and Applying Liquid Phase TEM

AbstractLiquid phase TEM has attracted widespread attention in recent years as a groundbreaking tool to address various fundamental problems in nanoscience. It has provided the opportunity to reveal many unseen dynamic phenomena of nanoscale materials in solution processes by direct imaging through liquids with high spatial and temporal resolution. After my earlier work on real‐time imaging of the nucleation, growth, and dynamic motion of nanoparticles in liquids by developing high‐resolution liquid phase transmission electron microscopy (TEM) down to the sub‐nanometer level, I established my own research group at Lawrence Berkeley National Lab in 2010. My group focuses on developing and applying liquid phase TEM to investigate complex systems and reactions. We have studied a set of scientific problems centered on understanding how atomic level heterogeneity and fluctuations at solid‐liquid interfaces impact nanoscale materials transformations using advanced liquid phase TEM. This article describes my personal journey in nanoscience, highlighting the main discoveries of my research group using liquid phase TEM as a unique tool. Some perspectives on the impacts of liquid phase TEM and the future opportunities in nanoscience and nanotechnology enabled by liquid phase TEM are also included.

Edge effect of synthetic antiferromagnetic skyrmion in nanotrack

Synthetic antiferromagnetic (SAF) skyrmions with high-speed mobility and stability might be used for building the next-generation spintronic devices and information storage, where the skyrmion Hall effect can be completely eliminated in the antiferromagnetically exchange-coupled magnetic bilayer. However, little research has been done about the influence of the edge effect on the generation and manipulation process of isolated synthetic antiferromagnetic skyrmion via spin-polarized current. Here, we study the influence of the edge effect on the current-induced generation and motion of skyrmion dynamics in SAF bilayer racetracks. Investigation results show that a stable SAF skyrmion can be successfully generated when the varying width of racetrack and the distance between the current injection position and the left side of racetrack are larger than a certain threshold. A wider racetrack leads to lower total energy and higher speed of the SAF skyrmion within a certain threshold, while a larger distance between the current injection position and the left side of the racetrack leads to lower total energy and velocity within another threshold. Our results are useful for understanding of SAF skyrmion physics and may provide a guidance for the nonvolatile computing devices.

Influence of liquid–vapor phase change on the self-propelled motion of droplets on wettability gradient surfaces

Self-propelled motion of sessile droplets on gradient surfaces is key to the advancement of microfluidic, nanofluidic, and surface fluidic technologies. Precise control over droplet dynamics, which often involves liquid–vapor phase transitions, is crucial for a variety of applications, including thermal management, self-cleaning surfaces, biochemical assays, and microreactors. Understanding how specific phase changes like condensation and evaporation affect droplet motion is essential for enhancing droplet manipulation and improving transport efficiency. We use the thermal Navier–Stokes–Korteweg equations to investigate the effects of condensation and evaporation on the motion and internal dynamics of droplets migrating across a surface with a linear surface energy profile. The study focuses on the early dynamics of self-propelled motion of a phase changing droplet at sub-micron scale before viscous forces are comparable with the gradient forces. Our results demonstrate that phase change significantly affects the self-propelled motion of droplets by reshaping interfacial mass flux distributions and internal flow dynamics. Condensation increases droplet volume and promotes extensive spreading toward regions of higher wettability, while evaporation reduces both volume and spreading. These changes in droplet shape and size directly affect the driving forces of motion, augmenting self-propulsion through condensation and suppressing it during evaporation. Additionally, each phase change type generates distinct internal flow patterns within the droplet, with condensation and evaporation exhibiting unique circulatory movements driven by localized phase changes near the contact lines.

Modeling the rupture dynamics of strong ground motion (> 1 g) in fault stepovers

Following the July 2019 Ridgecrest, California earthquakes, multiple field investigators noted that pebble- to boulder-sized rocks had been displaced from their position in the desert pavement within a stepover along the right-lateral strike-slip M7.1 rupture trace, without evidence of dragging or shearing. This implies localized ground motions in excess of 1 g, in contrast to the instrumentally recorded peak of 0.57 g. Similar rock displacement occurred in a stepover in the predominantly strike-slip 2010 M7.2 El Mayor-Cucapah earthquake. Together, these examples suggest that some aspect of how earthquake rupture negotiates a strike-slip fault stepover produces extremely localized strong ground acceleration. Here, we use the 3D finite element method to investigate how rupture through a variety of strike-slip stepover geometries influences strong ground acceleration. For subshear ruptures, we find that the presence of a stepover in general matters more than its dimensions; the strongest ground acceleration always occurs at the end of the first fault. For supershear ruptures, the stepover is effectively irrelevant, since the strongest particle acceleration occurs at the point of the supershear transition on the first fault. Our model subshear and supershear ruptures alike do produce horizontal particle acceleration above 1 g, but over a region so close to the fault (< 1 km) that a seismic network may not catch it. We suggest that the physics of rupture through a fault stepover could have been responsible for the displaced rocks in the Ridgecrest and El Mayor-Cucapah earthquakes, and that stepover regions may have particularly high ground motion hazard. Our study suggests that ground motion predictions and local hazard assessments should account for much stronger accelerations in the immediate near field of active faults, especially around stepovers and other geometrical discontinuities.

Time-Lapse Imaging of Asymmetric Spindle Positioning During Endothelial Tip Cell Division in Angiogenesis In Vivo.

The branching of new blood vessels by angiogenesis is critical to the development, growth, and repair of most vertebrate tissues and is frequently dysregulated in disease. At its core, angiogenesis is driven by the collective migration of leading "tip" and follower "stalk" endothelial cells. Recent work reveals that this collective movement is coordinated by asymmetric tip cell divisions that generate daughters of distinct size, signaling capacity and tip-stalk behaviors. Polarized mitotic spindle positioning is critical to generating such asymmetries in daughter cell size. However, the spatiotemporal dynamics of vertebrate spindle movement are often difficult to explore using in vivo systems. Here we describe a method for the sample preparation, live-imaging and data analysis of endothelial cell mitotic spindle positioning in developing zebrafish embryos. This method enables single-cell and population-level spindle dynamics to be monitored and quantified, both in wild-type or genetically/pharmacologically perturbed embryos. Moreover, this approach can be easily adapted for live imaging of spindle dynamics in other zebrafish embryonic tissues that experience similar asymmetric divisions, such as the trunk neural crest.

Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling.

Advancements in artificial intelligence (AI)-driven predictive modeling in dentistry are outpacing the clinical translation of research findings. Predictive modeling uses statistical methods to anticipate norms related to TMJ dynamics, complementing imaging modalities like cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI). Deep learning, a subset of AI, helps quantify and analyze complex hierarchical relationships in occlusion and TMJ function. This narrative review explores the application of predictive modeling and deep learning to identify clinical trends and associations related to occlusion and TMJ function. Debates persist regarding best practices for managing occlusal factors in temporomandibular joint (TMJ) function analysis while interpreting and quantifying findings related to the TMJ and occlusion and mitigating biases remain challenging. Data generated from noninvasive chairside tools such as jaw trackers, video tracking, and 3D scanners with virtual articulators offer unique insights by predicting variations in dynamic jaw movement, TMJ, and occlusion. The predictions help us understand the highly individualized norms surrounding TMJ function that are often required to address temporomandibular disorders (TMDs) in general practice. Normal TMJ function, occlusion, and the appropriate management of TMDs are complex and continue to attract ongoing debate. This review examines how predictive modeling and artificial intelligence aid in understanding occlusion and TMJ function and provides insights into complex dental conditions such as TMDs that may improve diagnosis and treatment outcomes with noninvasive techniques.

Technology Focus: Bits and Bottomhole Assemblies (December 2024)

Drillstring dynamics has been a central focus of research and mitigation efforts in the drilling industry for many years. The effect of dysfunction and poor dynamic motion extends far beyond simply reducing the lifespan of drilling equipment. When these issues degrade borehole quality to the extent that petrophysical measurements become compromised, managing these dynamics becomes crucial. Addressing drillstring dynamics in these cases effectively can be the key to improving our understanding of reservoir characterization, which directly affects hydrocarbon-recovery strategies. Neglecting to manage these dynamics thus not only risks immediate operational setbacks but also threatens to diminish overall hydrocarbon recovery. The integration of the latest state-of-the-art technology in modeling and measurement of drilling dynamics and borehole quality has enhanced the understanding of bit, bottomhole assembly (BHA), and drillstring design. This can be seen in recent work on analysis of cutter-formation interaction, borehole-quality improvement, and modeling improvements. This progress allows drilling teams to make more-informed, proactive adjustments to drilling parameters, thereby avoiding the negative consequences of dysfunction. As the drilling industry shifts its focus toward geothermal drilling, understanding and modeling drillstring dynamics takes on even greater importance. Geothermal drilling frequently encounters harder igneous and metamorphic rocks, which pose unique challenges. In these conditions, the performance of the drill bit, BHA, and drillstring becomes critical. Accurate modeling and effective management of these dynamics are essential to optimizing drilling efficiency and minimizing the risk of equipment loss or operational failures. In this context, ongoing research and innovation in both drillstring dynamics and its modeling are vital to the success of geothermal, hydrocarbon, and other drilling projects. Yet the best mitigation applied may not be the latest but existing tried and tested equipment. Recommended additional reading at OnePetro: www.onepetro.org. OTC 35320 Applications, Results, and Lessons Learned From Using Roller Reamers in Hard, Abrasive Formations—The FORGE Project Application by E. Almanza, Redback Drilling Tools, et al. SPE 217939 Avoiding High Local Doglegs by Integrating the Drill Bit Into the Rotary Steerable System by M. Hahn, Baker Hughes, et al. SPE 220789 Modeling PDC Cutter Loads When Drilling Interbedded Formations at Constant Penetration per Revolution vs. Weight on Bit by Paul E. Pastusek, Pastusek and Associates, et al.

Unsupervised 3D Object Segmentation of Point Clouds by Geometry Consistency.

In this paper, we study the problem of 3D object segmentation from raw point clouds. Unlike existing methods which usually require a large amount of human annotations for full supervision, we propose the first unsupervised method, called OGC, to simultaneously identify multiple 3D objects in a single forward pass, without needing any type of human annotations. The key to our approach is to fully leverage the dynamic motion patterns over sequential point clouds as supervision signals to automatically discover rigid objects. Our method consists of three major components, 1) the object segmentation network to directly estimate multi-object masks from a single point cloud frame, 2) the auxiliary self-supervised scene flow estimator, and 3) our core object geometry consistency component. By carefully designing a series of loss functions, we effectively take into account the multi-object rigid consistency and the object shape invariance in both temporal and spatial scales. This allows our method to truly discover the object geometry even in the absence of annotations. We extensively evaluate our method on five datasets, demonstrating the superior performance for object part instance segmentation and general object segmentation in both indoor and the challenging outdoor scenarios.

Dynamic Movement Primitives-Based Human Action Prediction and Shared Control for Bilateral Robot Teleoperation

Dynamic Movement Primitives-Based Human Action Prediction and Shared Control for Bilateral Robot Teleoperation

Identifying Parkinson’s disease and its stages using static standing balance

The current assessment of Parkinson’s disease (PD) relies on dynamic motor tasks, limiting accessibility. This study aimed to propose an innovative approach to identifying PD and its stages using static standing balance and machine learning. A total of 210 participants were recruited, including a control group and five PD groups categorized by stage. Each participant completed a 10-s static standing balance task in which center of pressure trajectory data in the medial-lateral and anterior-posterior directions were collected. Features were extracted from these trajectory data and the data derived from them using both representation learning and handcrafting methods. A Transformer encoder-based classifier was trained on these features and achieved an F1-score of 0.963 in classifying the six study groups. This approach enhances the accessibility of PD assessment, enabling earlier detection and timely intervention. The novel data mining framework introduced in this study heralds a new era of time-series data-driven digital healthcare.

Multi-UAV Collaborative Target Search Method in Unknown Dynamic Environment

The challenge of search inefficiency arises when multiple UAV swarms conduct dynamic target area searches in unknown environments. The primary sources of this inefficiency are repeated searches in the target region and the dynamic motion of targets. To address this issue, we present the distributed adaptive real-time planning search (DAPSO) technique, which enhances the search efficiency for dynamic targets in uncertain mission situations. To minimize repeated searches, UAVs utilize localized communication for information exchange and dynamically update their situational awareness regarding the mission environment, facilitating collaborative exploration. To mitigate the effects of target mobility, we develop a dynamic mission planning method based on local particle swarm optimization, enabling UAVs to adjust their search trajectories in response to real-time environmental inputs. Finally, we propose a distance-based inter-vehicle collision avoidance strategy to ensure safety during multi-UAV cooperative searches. The experimental findings demonstrate that the proposed DAPSO method significantly outperforms other search strategies regarding the coverage and target detection rates.

Bayesian Fictitious Play in Oligopoly: The Case of Risk-Averse Agents

A number of learning models have been suggested to analyze the repeated interaction of boundedly rational agents competing in oligopolistic markets. The agents form a model of the environment that they are competing in, which includes the market demand and price formation process, as well as their expectations of their rivals’ actions. The agents update their model based on the observed output and price realizations and then choose their next period output levels according to an optimization criterion. In previous works, the global dynamics of price movement have been analyzed when risk-neutral agents maximize their expected rewards at each round. However, in many practical settings, agents may be concerned with the risk or uncertainty in their reward stream, in addition to the expected value of the future rewards. Learning in oligopoly models for the case of risk-averse agents has received much less attention. In this paper, we present a novel learning model that extends fictitious play learning to continuous strategy spaces where agents combine their prior beliefs with market price realizations in previous periods to learn the mean and the variance of the aggregate supply function of the rival firms in a Bayesian framework. Next, each firm maximizes a linear combination of the expected value of the profit and a penalty term for the variance of the returns. Specifically, each agent assumes that the aggregate supply of the remaining agents is sampled from a parametric distribution employing a normal-inverse gamma prior. We prove the convergence of the proposed dynamics and present simulation results to compare the proposed learning rule to the traditional best response dynamics.

Examining the network dynamics of daily movement and dietary behaviors among college students: A diary study.

Promoting individuals' overall health and well-being is important, and understanding the interconnections between daily movement and dietary behaviors may provide insights for developing effective health behavior interventions. In the current study, we therefore adopted a network approach to investigate the complex relationships among movement and dietary behaviors within a daily diary study. Data were collected from 101 college students over a 28-day period, assessing movement (i.e., physical exercise and sedentary behavior) and dietary (i.e., overeating, sugar-sweetened beverage intake, and snack consumption) behaviors. We employed a multilevel vector autoregressive model to analyze the within-person (temporal and contemporaneous) and between-person networks of movement and dietary behaviors. Our findings unveiled a negative association between physical exercise and sedentary behavior at both contemporaneous and between-person levels, while the interconnections among dietary behaviors displayed nuanced variations across different levels. We also found intricate relationships between movement and dietary behaviors, with sedentary behavior and sugar-sweetened beverage intake emerging as central nodes in the behavior networks. This exploratory study underscores the complex interconnections of daily health behaviors, particularly highlighting the potential roles of sedentary behavior and sugar-sweetened beverage intake in multiple behavior interventions. These preliminary findings have yet to be validated through theory-driven studies with experimental designs.

Dynamic Facial Reanimation for Facial Palsy: The Oman experience.

The goal of facial reanimation for facial palsy is to restore resting facial symmetry and dynamic facial motion that mirrors the opposite side as closely as possible. This study aimed to evaluate the restoration of oral commissure symmetry at rest and during excursion among patients with facial paralysis treated with free gracilis muscle transfer. This study included 9 patients who underwent facial reanimation with free gracilis muscle transfer at Khoula Hospital, Muscat, Oman, from 2019 to 2022. Children under 14 underwent a 2-stage surgery, while those above 14 underwent single-stage reconstruction. The average age among the cohorts was 24 years. Overall, 5 cases underwent a 2-stage facial animation, 4 underwent single-stage reconstruction and 1 patient had free flap loss following the free gracilis muscle transfer. The mean time for noticing recovery was 3 months postoperatively. Early recovery was noted in patients who underwent single-stage free gracilis muscle transfer with motor innervation from the ipsilateral nerve to the masseter compared to the cross-facial nerve transfer. Good patient satisfaction (88.9%) was observed following the procedure. This study observed earlier recovery in patients who had undergone single-stage free gracilis muscle transfer with motor innervation from the ipsilateral nerve to the masseter compared to the cross-facial nerve transfer. The oral commissure symmetry at rest and during excursion among patients with facial paralysis treated with free gracilis muscle transfer in Oman was found to be near normal.

Exploring the Impact of Battery Charge Reduction Rate and the Placement of Chargers on AGV Operation

This paper presents a simulation model to study the effect of the battery charging rate of Automated Guided Vehicles (AGVs) on the overall output of a toy car production environment. This paper uses Modular Petri Nets for modeling and the General Petri Net Simulator (GPenSIM) for model implementation on MATLAB and simulation. The main focus of this paper is to analyze the operational efficiency of AGVs under varying conditions, such as the impact of battery charge reduction rates and the strategic placement of Charging Stations within the production line. By employing Modular Petri Nets implemented with GPenSIM, this paper presents a detailed model that captures the dynamics (movements and interactions) of AGVs in a simulated manufacturing environment. The model is also extensible, as newer functionalities can be added to it as Petri Modules. This paper specifically focuses on two critical operational parameters: (a) the number of AGVs and their battery charge reduction rate; (b) the number of Charging Stations. In summary, the goal, aim, and novelty of this paper is to provide a simpler yet effective model to practitioners so that they can study and experiment without needing advanced mathematical skills.

Instrumented swim test for quantifying motor impairment in rodents.

Swim tests are highly effective for identifying vestibular deficits in rodents by offering significant vestibular motor challenges with reduced proprioceptive input, unlike rotarod and balance beam tests. Traditional swim tests rely on subjective assessments, limiting objective quantification and reproducibility. We present a novel instrumented swim test using a miniature motion sensor with a 3D accelerometer and 3D gyroscope affixed to the rodent's head. This setup robustly quantifies six-dimensional motion-three translational and three rotational axes-during swimming with high temporal resolution. We demonstrate the test's capabilities by comparing head movements of Gpr156-/- mutant mice, which have impaired otolith organ development, to their heterozygous littermates. Our results show axis-specific differences in head movement probability distribution functions and dynamics that identify mice with the Gpr156 mutation. Axis-specific power spectrum analyses reveal selective movement alterations within distinct frequency ranges. Additionally, our spherical visualization and 3D analysis quantifies swimming performance based on head vector distance from upright. We use this analysis to generate a single classifier metric-a weighted average of an animal's head deviation from upright during swimming. This metric effectively distinguishes animals with vestibular dysfunction from those with normal vestibular function. Overall, this instrumented swim test provides quantitative metrics for assessing performance and identifying subtle, axis- and frequency-specific deficits not captured by existing systems. This novel quantitative approach can enhance understanding of rodent sensorimotor function including enabling more selective and reproducible studies of vestibular-motor deficits.